Self-Driving Car Engineer Nanodegree

Deep Learning

Project: Build a Traffic Sign Recognition Classifier

In this notebook, a template is provided for you to implement your functionality in stages which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission, if necessary. Sections that begin with 'Implementation' in the header indicate where you should begin your implementation for your project. Note that some sections of implementation are optional, and will be marked with 'Optional' in the header.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.

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Step 1: Dataset Exploration

Visualize the German Traffic Signs Dataset. This is open ended, some suggestions include: plotting traffic signs images, plotting the count of each sign, etc. Be creative!

The pickled data is a dictionary with 4 key/value pairs:

• features -> the images pixel values, (width, height, channels)
• labels -> the label of the traffic sign
• sizes -> the original width and height of the image, (width, height)
• coords -> coordinates of a bounding box around the sign in the image, (x1, y1, x2, y2). Based the original image (not the resized version).

%%bash

wget https://d17h27t6h515a5.cloudfront.net/topher/2016/October/580d53ce_traffic-sign-data/traffic-sign-data.zip

--2016-11-12 16:35:24--  https://d17h27t6h515a5.cloudfront.net/topher/2016/October/580d53ce_traffic-sign-data/traffic-sign-data.zip
Resolving d17h27t6h515a5.cloudfront.net (d17h27t6h515a5.cloudfront.net)... 54.230.141.64, 54.230.141.136, 54.230.141.85, ...
Connecting to d17h27t6h515a5.cloudfront.net (d17h27t6h515a5.cloudfront.net)|54.230.141.64|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 123550596 (118M)
Saving to: ‘traffic-sign-data.zip’

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 12600K .......... .......... .......... .......... .......... 10% 5.48M 23s
 12650K .......... .......... .......... .......... .......... 10% 61.1M 23s
 12700K .......... .......... .......... .......... .......... 10% 15.0M 23s
 12750K .......... .......... .......... .......... .......... 10% 7.95M 23s
 12800K .......... .......... .......... .......... .......... 10% 21.3M 23s
 12850K .......... .......... .......... .......... .......... 10% 6.21M 23s
 12900K .......... .......... .......... .......... .......... 10% 23.3M 23s
 12950K .......... .......... .......... .......... .......... 10% 6.34M 23s
 13000K .......... .......... .......... .......... .......... 10% 51.0M 23s
 13050K .......... .......... .......... .......... .......... 10% 11.0M 23s
 13100K .......... .......... .......... .......... .......... 10% 5.93M 23s
 13150K .......... .......... .......... .......... .......... 10% 64.4M 23s
 13200K .......... .......... .......... .......... .......... 10% 13.4M 23s
 13250K .......... .......... .......... .......... .......... 11% 8.24M 23s
 13300K .......... .......... .......... .......... .......... 11% 11.6M 22s
 13350K .......... .......... .......... .......... .......... 11% 11.6M 22s
 13400K .......... .......... .......... .......... .......... 11% 11.0M 22s
 13450K .......... .......... .......... .......... .......... 11% 7.56M 22s
 13500K .......... .......... .......... .......... .......... 11% 23.4M 22s
 13550K .......... .......... .......... .......... .......... 11% 11.6M 22s
 13600K .......... .......... .......... .......... .......... 11% 11.5M 22s
 13650K .......... .......... .......... .......... .......... 11% 8.52M 22s
 13700K .......... .......... .......... .......... .......... 11% 11.6M 22s
 13750K .......... .......... .......... .......... .......... 11% 11.5M 22s
 13800K .......... .......... .......... .......... .......... 11% 11.1M 22s
 13850K .......... .......... .......... .......... .......... 11% 11.5M 22s
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 13950K .......... .......... .......... .......... .......... 11% 11.5M 22s
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 14050K .......... .......... .......... .......... .......... 11% 8.52M 22s
 14100K .......... .......... .......... .......... .......... 11% 11.1M 22s
 14150K .......... .......... .......... .......... .......... 11% 11.5M 22s
 14200K .......... .......... .......... .......... .......... 11% 11.6M 21s
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 14300K .......... .......... .......... .......... .......... 11% 11.0M 21s
 14350K .......... .......... .......... .......... .......... 11% 4.61M 21s
 14400K .......... .......... .......... .......... .......... 11% 63.8M 21s
 14450K .......... .......... .......... .......... .......... 12% 17.2M 21s
 14500K .......... .......... .......... .......... .......... 12% 11.5M 21s
 14550K .......... .......... .......... .......... .......... 12% 11.1M 21s
 14600K .......... .......... .......... .......... .......... 12% 12.1M 21s
 14650K .......... .......... .......... .......... .......... 12% 11.0M 21s
 14700K .......... .......... .......... .......... .......... 12% 8.99M 21s
 14750K .......... .......... .......... .......... .......... 12% 15.1M 21s
 14800K .......... .......... .......... .......... .......... 12% 12.1M 21s
 14850K .......... .......... .......... .......... .......... 12% 8.52M 21s
 14900K .......... .......... .......... .......... .......... 12% 11.6M 21s
 14950K .......... .......... .......... .......... .......... 12% 11.0M 21s
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 15050K .......... .......... .......... .......... .......... 12% 11.0M 21s
 15100K .......... .......... .......... .......... .......... 12% 11.6M 21s
 15150K .......... .......... .......... .......... .......... 12% 11.6M 21s
 15200K .......... .......... .......... .......... .......... 12% 11.2M 20s
 15250K .......... .......... .......... .......... .......... 12% 8.82M 20s
 15300K .......... .......... .......... .......... .......... 12% 11.6M 20s
 15350K .......... .......... .......... .......... .......... 12% 11.1M 20s
 15400K .......... .......... .......... .......... .......... 12% 11.6M 20s
 15450K .......... .......... .......... .......... .......... 12% 8.41M 20s
 15500K .......... .......... .......... .......... .......... 12% 9.97M 20s
 15550K .......... .......... .......... .......... .......... 12% 21.4M 20s
 15600K .......... .......... .......... .......... .......... 12% 6.33M 20s
 15650K .......... .......... .......... .......... .......... 13% 5.76M 20s
 15700K .......... .......... .......... .......... .......... 13% 63.5M 20s
 15750K .......... .......... .......... .......... .......... 13% 5.95M 20s
 15800K .......... .......... .......... .......... .......... 13% 63.1M 20s
 15850K .......... .......... .......... .......... .......... 13% 11.9M 20s
 15900K .......... .......... .......... .......... .......... 13% 23.9M 20s
 15950K .......... .......... .......... .......... .......... 13% 9.10M 20s
 16000K .......... .......... .......... .......... .......... 13% 18.4M 20s
 16050K .......... .......... .......... .......... .......... 13% 8.73M 20s
 16100K .......... .......... .......... .......... .......... 13% 11.6M 20s
 16150K .......... .......... .......... .......... .......... 13% 11.6M 20s
 16200K .......... .......... .......... .......... .......... 13% 11.1M 20s
 16250K .......... .......... .......... .......... .......... 13% 11.5M 20s
 16300K .......... .......... .......... .......... .......... 13% 11.0M 20s
 16350K .......... .......... .......... .......... .......... 13% 12.1M 20s
 16400K .......... .......... .......... .......... .......... 13% 11.1M 19s
 16450K .......... .......... .......... .......... .......... 13% 8.52M 19s
 16500K .......... .......... .......... .......... .......... 13% 11.6M 19s
 16550K .......... .......... .......... .......... .......... 13% 11.6M 19s
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 16650K .......... .......... .......... .......... .......... 13% 11.0M 19s
 16700K .......... .......... .......... .......... .......... 13% 11.6M 19s
 16750K .......... .......... .......... .......... .......... 13% 11.5M 19s
 16800K .......... .......... .......... .......... .......... 13% 11.1M 19s
 16850K .......... .......... .......... .......... .......... 14% 7.28M 19s
 16900K .......... .......... .......... .......... .......... 14% 15.0M 19s
 16950K .......... .......... .......... .......... .......... 14% 11.5M 19s
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 17050K .......... .......... .......... .......... .......... 14% 11.1M 19s
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 17200K .......... .......... .......... .......... .......... 14% 11.1M 19s
 17250K .......... .......... .......... .......... .......... 14% 8.67M 19s
 17300K .......... .......... .......... .......... .......... 14% 11.7M 19s
 17350K .......... .......... .......... .......... .......... 14% 11.0M 19s
 17400K .......... .......... .......... .......... .......... 14% 11.6M 19s
 17450K .......... .......... .......... .......... .......... 14% 11.0M 19s
 17500K .......... .......... .......... .......... .......... 14% 11.6M 19s
 17550K .......... .......... .......... .......... .......... 14% 11.6M 19s
 17600K .......... .......... .......... .......... .......... 14% 11.6M 19s
 17650K .......... .......... .......... .......... .......... 14% 8.24M 19s
 17700K .......... .......... .......... .......... .......... 14% 11.6M 18s
 17750K .......... .......... .......... .......... .......... 14% 11.5M 18s
 17800K .......... .......... .......... .......... .......... 14% 11.5M 18s
 17850K .......... .......... .......... .......... .......... 14% 11.0M 18s
 17900K .......... .......... .......... .......... .......... 14% 11.5M 18s
 17950K .......... .......... .......... .......... .......... 14% 11.1M 18s
 18000K .......... .......... .......... .......... .......... 14% 12.1M 18s
 18050K .......... .......... .......... .......... .......... 15% 5.50M 18s
 18100K .......... .......... .......... .......... .......... 15% 43.0M 18s
 18150K .......... .......... .......... .......... .......... 15% 11.6M 18s
 18200K .......... .......... .......... .......... .......... 15% 11.1M 18s
 18250K .......... .......... .......... .......... .......... 15% 11.5M 18s
 18300K .......... .......... .......... .......... .......... 15% 11.6M 18s
 18350K .......... .......... .......... .......... .......... 15% 11.0M 18s
 18400K .......... .......... .......... .......... .......... 15% 11.6M 18s
 18450K .......... .......... .......... .......... .......... 15% 8.82M 18s
 18500K .......... .......... .......... .......... .......... 15% 11.1M 18s
 18550K .......... .......... .......... .......... .......... 15% 11.6M 18s
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 18650K .......... .......... .......... .......... .......... 15% 12.0M 18s
 18700K .......... .......... .......... .......... .......... 15% 10.6M 18s
 18750K .......... .......... .......... .......... .......... 15% 12.1M 18s
 18800K .......... .......... .......... .......... .......... 15%  333K 19s
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 19350K .......... .......... .......... .......... .......... 16% 1.22M 18s
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 20150K .......... .......... .......... .......... .......... 16% 7.40M 17s
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 20350K .......... .......... .......... .......... .......... 16% 96.6M 17s
 20400K .......... .......... .......... .......... .......... 16% 4.83M 17s
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 20800K .......... .......... .......... .......... .......... 17% 5.48M 17s
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 21050K .......... .......... .......... .......... .......... 17% 63.1M 17s
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 21650K .......... .......... .......... .......... .......... 17% 92.5M 17s
 21700K .......... .......... .......... .......... .......... 18% 99.9M 17s
 21750K .......... .......... .......... .......... .......... 18% 2.20M 17s
 21800K .......... .......... .......... .......... .......... 18% 14.4M 17s
 21850K .......... .......... .......... .......... .......... 18% 3.61M 17s
 21900K .......... .......... .......... .......... .......... 18%  125M 17s
 21950K .......... .......... .......... .......... .......... 18% 9.63M 17s
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 22050K .......... .......... .......... .......... .......... 18% 4.75M 17s
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 22150K .......... .......... .......... .......... .......... 18% 4.79M 17s
 22200K .......... .......... .......... .......... .......... 18%  126M 17s
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 22500K .......... .......... .......... .......... .......... 18% 97.7M 17s
 22550K .......... .......... .......... .......... .......... 18% 4.75M 17s
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 23050K .......... .......... .......... .......... .......... 19% 4.83M 17s
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 23300K .......... .......... .......... .......... .......... 19% 91.9M 17s
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 24300K .......... .......... .......... .......... .......... 20% 38.6M 16s
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 24600K .......... .......... .......... .......... .......... 20% 96.2M 16s
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 25300K .......... .......... .......... .......... .......... 21% 4.81M 16s
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 25900K .......... .......... .......... .......... .......... 21% 96.5M 16s
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 26700K .......... .......... .......... .......... .......... 22% 88.2M 16s
 26750K .......... .......... .......... .......... .......... 22% 4.84M 16s
 26800K .......... .......... .......... .......... .......... 22% 37.0M 16s
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 41700K .......... .......... .......... .......... .......... 34%  103M 13s
 41750K .......... .......... .......... .......... .......... 34% 7.28M 13s
 41800K .......... .......... .......... .......... .......... 34% 2.29M 13s
 41850K .......... .......... .......... .......... .......... 34%  207M 13s
 41900K .......... .......... .......... .......... .......... 34% 5.11M 13s
 41950K .......... .......... .......... .......... .......... 34% 14.4M 13s
 42000K .......... .......... .......... .......... .......... 34% 7.48M 13s
 42050K .......... .......... .......... .......... .......... 34% 4.18M 13s
 42100K .......... .......... .......... .......... .......... 34% 4.85M 13s
 42150K .......... .......... .......... .......... .......... 34% 7.30M 13s
 42200K .......... .......... .......... .......... .......... 35% 10.2M 13s
 42250K .......... .......... .......... .......... .......... 35% 11.5M 13s
 42300K .......... .......... .......... .......... .......... 35% 4.52M 13s
 42350K .......... .......... .......... .......... .......... 35% 4.75M 13s
 42400K .......... .......... .......... .......... .......... 35% 4.50M 13s
 42450K .......... .......... .......... .......... .......... 35% 4.69M 13s
 42500K .......... .......... .......... .......... .......... 35% 4.31M 13s
 42550K .......... .......... .......... .......... .......... 35% 97.2M 13s
 42600K .......... .......... .......... .......... .......... 35% 4.67M 13s
 42650K .......... .......... .......... .......... .......... 35% 6.95M 13s
 42700K .......... .......... .......... .......... .......... 35% 9.80M 13s
 42750K .......... .......... .......... .......... .......... 35% 3.42M 13s
 42800K .......... .......... .......... .......... .......... 35% 90.3M 13s
 42850K .......... .......... .......... .......... .......... 35% 4.77M 13s
 42900K .......... .......... .......... .......... .......... 35% 4.74M 13s
 42950K .......... .......... .......... .......... .......... 35% 4.64M 13s
 43000K .......... .......... .......... .......... .......... 35% 4.45M 13s
 43050K .......... .......... .......... .......... .......... 35%  118M 13s
 43100K .......... .......... .......... .......... .......... 35% 4.71M 13s
 43150K .......... .......... .......... .......... .......... 35% 8.04M 13s
 43200K .......... .......... .......... .......... .......... 35% 3.52M 13s
 43250K .......... .......... .......... .......... .......... 35% 9.47M 13s
 43300K .......... .......... .......... .......... .......... 35% 8.76M 13s
 43350K .......... .......... .......... .......... .......... 35% 10.6M 13s
 43400K .......... .......... .......... .......... .......... 36% 4.44M 13s
 43450K .......... .......... .......... .......... .......... 36% 4.74M 13s
 43500K .......... .......... .......... .......... .......... 36% 4.41M 13s
 43550K .......... .......... .......... .......... .......... 36%  134M 13s
 43600K .......... .......... .......... .......... .......... 36% 4.67M 13s
 43650K .......... .......... .......... .......... .......... 36% 4.76M 13s
 43700K .......... .......... .......... .......... .......... 36% 4.69M 13s
 43750K .......... .......... .......... .......... .......... 36% 4.79M 13s
 43800K .......... .......... .......... .......... .......... 36% 4.78M 13s
 43850K .......... .......... .......... .......... .......... 36%  117M 13s
 43900K .......... .......... .......... .......... .......... 36% 4.80M 13s
 43950K .......... .......... .......... .......... .......... 36% 8.56M 13s
 44000K .......... .......... .......... .......... .......... 36% 3.59M 13s
 44050K .......... .......... .......... .......... .......... 36% 4.76M 13s
 44100K .......... .......... .......... .......... .......... 36% 4.72M 13s
 44150K .......... .......... .......... .......... .......... 36% 94.7M 13s
 44200K .......... .......... .......... .......... .......... 36% 8.33M 13s
 44250K .......... .......... .......... .......... .......... 36% 3.76M 13s
 44300K .......... .......... .......... .......... .......... 36% 4.75M 13s
 44350K .......... .......... .......... .......... .......... 36%  132M 13s
 44400K .......... .......... .......... .......... .......... 36% 4.80M 13s
 44450K .......... .......... .......... .......... .......... 36% 6.87M 13s
 44500K .......... .......... .......... .......... .......... 36% 4.07M 13s
 44550K .......... .......... .......... .......... .......... 36% 4.81M 13s
 44600K .......... .......... .......... .......... .......... 37% 4.75M 13s
 44650K .......... .......... .......... .......... .......... 37%  119M 13s
 44700K .......... .......... .......... .......... .......... 37% 4.82M 13s
 44750K .......... .......... .......... .......... .......... 37% 19.8M 13s
 44800K .......... .......... .......... .......... .......... 37% 3.04M 13s
 44850K .......... .......... .......... .......... .......... 37% 4.78M 13s
 44900K .......... .......... .......... .......... .......... 37% 4.81M 13s
 44950K .......... .......... .......... .......... .......... 37% 93.5M 13s
 45000K .......... .......... .......... .......... .......... 37% 4.82M 13s
 45050K .......... .......... .......... .......... .......... 37% 4.80M 13s
 45100K .......... .......... .......... .......... .......... 37%  103M 13s
 45150K .......... .......... .......... .......... .......... 37% 7.11M 13s
 45200K .......... .......... .......... .......... .......... 37% 2.29M 13s
 45250K .......... .......... .......... .......... .......... 37%  167M 13s
 45300K .......... .......... .......... .......... .......... 37% 4.69M 13s
 45350K .......... .......... .......... .......... .......... 37% 10.2M 13s
 45400K .......... .......... .......... .......... .......... 37% 3.43M 13s
 45450K .......... .......... .......... .......... .......... 37%  115M 13s
 45500K .......... .......... .......... .......... .......... 37% 4.85M 13s
 45550K .......... .......... .......... .......... .......... 37% 6.88M 13s
 45600K .......... .......... .......... .......... .......... 37% 8.77M 13s
 45650K .......... .......... .......... .......... .......... 37% 3.22M 13s
 45700K .......... .......... .......... .......... .......... 37%  125M 12s
 45750K .......... .......... .......... .......... .......... 37% 4.73M 12s
 45800K .......... .......... .......... .......... .......... 38% 4.22M 12s
 45850K .......... .......... .......... .......... .......... 38% 4.70M 12s
 45900K .......... .......... .......... .......... .......... 38% 17.0M 12s
 45950K .......... .......... .......... .......... .......... 38% 6.32M 12s
 46000K .......... .......... .......... .......... .......... 38% 10.6M 12s
 46050K .......... .......... .......... .......... .......... 38% 3.97M 12s
 46100K .......... .......... .......... .......... .......... 38% 4.79M 12s
 46150K .......... .......... .......... .......... .......... 38% 4.19M 12s
 46200K .......... .......... .......... .......... .......... 38% 4.78M 12s
 46250K .......... .......... .......... .......... .......... 38% 88.8M 12s
 46300K .......... .......... .......... .......... .......... 38% 6.89M 12s
 46350K .......... .......... .......... .......... .......... 38% 3.75M 12s
 46400K .......... .......... .......... .......... .......... 38% 4.79M 12s
 46450K .......... .......... .......... .......... .......... 38% 26.0M 12s
 46500K .......... .......... .......... .......... .......... 38% 5.78M 12s
 46550K .......... .......... .......... .......... .......... 38% 10.6M 12s
 46600K .......... .......... .......... .......... .......... 38% 4.20M 12s
 46650K .......... .......... .......... .......... .......... 38% 4.81M 12s
 46700K .......... .......... .......... .......... .......... 38% 4.61M 12s
 46750K .......... .......... .......... .......... .......... 38%  122M 12s
 46800K .......... .......... .......... .......... .......... 38% 4.43M 12s
 46850K .......... .......... .......... .......... .......... 38% 4.73M 12s
 46900K .......... .......... .......... .......... .......... 38% 4.54M 12s
 46950K .......... .......... .......... .......... .......... 38% 4.84M 12s
 47000K .......... .......... .......... .......... .......... 38% 7.99M 12s
 47050K .......... .......... .......... .......... .......... 39% 9.04M 12s
 47100K .......... .......... .......... .......... .......... 39% 9.16M 12s
 47150K .......... .......... .......... .......... .......... 39% 3.85M 12s
 47200K .......... .......... .......... .......... .......... 39% 4.78M 12s
 47250K .......... .......... .......... .......... .......... 39% 24.6M 12s
 47300K .......... .......... .......... .......... .......... 39% 6.55M 12s
 47350K .......... .......... .......... .......... .......... 39% 4.36M 12s
 47400K .......... .......... .......... .......... .......... 39% 4.79M 12s
 47450K .......... .......... .......... .......... .......... 39% 10.1M 12s
 47500K .......... .......... .......... .......... .......... 39% 8.30M 12s
 47550K .......... .......... .......... .......... .......... 39% 10.6M 12s
 47600K .......... .......... .......... .......... .......... 39% 4.33M 12s
 47650K .......... .......... .......... .......... .......... 39% 4.75M 12s
 47700K .......... .......... .......... .......... .......... 39% 4.57M 12s
 47750K .......... .......... .......... .......... .......... 39% 4.82M 12s
 47800K .......... .......... .......... .......... .......... 39%  128M 12s
 47850K .......... .......... .......... .......... .......... 39% 4.72M 12s
 47900K .......... .......... .......... .......... .......... 39% 4.84M 12s
 47950K .......... .......... .......... .......... .......... 39% 4.80M 12s
 48000K .......... .......... .......... .......... .......... 39% 4.78M 12s
 48050K .......... .......... .......... .......... .......... 39% 4.72M 12s
 48100K .......... .......... .......... .......... .......... 39%  128M 12s
 48150K .......... .......... .......... .......... .......... 39% 4.80M 12s
 48200K .......... .......... .......... .......... .......... 39% 4.80M 12s
 48250K .......... .......... .......... .......... .......... 40% 28.1M 12s
 48300K .......... .......... .......... .......... .......... 40% 2.77M 12s
 48350K .......... .......... .......... .......... .......... 40%  102M 12s
 48400K .......... .......... .......... .......... .......... 40% 4.84M 12s
 48450K .......... .......... .......... .......... .......... 40% 8.28M 12s
 48500K .......... .......... .......... .......... .......... 40% 3.83M 12s
 48550K .......... .......... .......... .......... .......... 40% 4.78M 12s
 48600K .......... .......... .......... .......... .......... 40%  128M 12s
 48650K .......... .......... .......... .......... .......... 40% 4.80M 12s
 48700K .......... .......... .......... .......... .......... 40% 4.78M 12s
 48750K .......... .......... .......... .......... .......... 40% 4.86M 12s
 48800K .......... .......... .......... .......... .......... 40% 4.75M 12s
 48850K .......... .......... .......... .......... .......... 40% 88.7M 12s
 48900K .......... .......... .......... .......... .......... 40% 7.13M 12s
 48950K .......... .......... .......... .......... .......... 40% 4.39M 12s
 49000K .......... .......... .......... .......... .......... 40% 4.75M 12s
 49050K .......... .......... .......... .......... .......... 40% 4.78M 12s
 49100K .......... .......... .......... .......... .......... 40%  133M 12s
 49150K .......... .......... .......... .......... .......... 40% 4.74M 12s
 49200K .......... .......... .......... .......... .......... 40% 4.85M 12s
 49250K .......... .......... .......... .......... .......... 40% 4.77M 12s
 49300K .......... .......... .......... .......... .......... 40% 29.1M 12s
 49350K .......... .......... .......... .......... .......... 40% 2.95M 12s
 49400K .......... .......... .......... .......... .......... 40%  120M 12s
 49450K .......... .......... .......... .......... .......... 41% 4.81M 12s
 49500K .......... .......... .......... .......... .......... 41% 4.76M 12s
 49550K .......... .......... .......... .......... .......... 41% 4.84M 12s
 49600K .......... .......... .......... .......... .......... 41% 4.80M 12s
 49650K .......... .......... .......... .......... .......... 41% 4.76M 12s
 49700K .......... .......... .......... .......... .......... 41% 98.4M 12s
 49750K .......... .......... .......... .......... .......... 41% 4.83M 12s
 49800K .......... .......... .......... .......... .......... 41% 19.0M 12s
 49850K .......... .......... .......... .......... .......... 41% 3.13M 12s
 49900K .......... .......... .......... .......... .......... 41%  125M 12s
 49950K .......... .......... .......... .......... .......... 41% 4.75M 12s
 50000K .......... .......... .......... .......... .......... 41% 4.81M 12s
 50050K .......... .......... .......... .......... .......... 41% 4.75M 12s
 50100K .......... .......... .......... .......... .......... 41% 4.81M 12s
 50150K .......... .......... .......... .......... .......... 41% 4.78M 12s
 50200K .......... .......... .......... .......... .......... 41%  130M 12s
 50250K .......... .......... .......... .......... .......... 41% 9.39M 12s
 50300K .......... .......... .......... .......... .......... 41% 3.85M 12s
 50350K .......... .......... .......... .......... .......... 41% 4.83M 12s
 50400K .......... .......... .......... .......... .......... 41% 4.80M 12s
 50450K .......... .......... .......... .......... .......... 41% 4.70M 12s
 50500K .......... .......... .......... .......... .......... 41%  125M 12s
 50550K .......... .......... .......... .......... .......... 41% 4.85M 12s
 50600K .......... .......... .......... .......... .......... 41% 4.81M 12s
 50650K .......... .......... .......... .......... .......... 42% 4.74M 12s
 50700K .......... .......... .......... .......... .......... 42%  124M 12s
 50750K .......... .......... .......... .......... .......... 42% 4.79M 12s
 50800K .......... .......... .......... .......... .......... 42% 99.3M 12s
 50850K .......... .......... .......... .......... .......... 42% 2.87M 12s
 50900K .......... .......... .......... .......... .......... 42% 4.79M 12s
 50950K .......... .......... .......... .......... .......... 42% 4.78M 12s
 51000K .......... .......... .......... .......... .......... 42%  127M 12s
 51050K .......... .......... .......... .......... .......... 42% 4.82M 12s
 51100K .......... .......... .......... .......... .......... 42% 4.74M 12s
 51150K .......... .......... .......... .......... .......... 42% 4.83M 12s
 51200K .......... .......... .......... .......... .......... 42%  125M 12s
 51250K .......... .......... .......... .......... .......... 42% 2.44M 12s
 51300K .......... .......... .......... .......... .......... 42% 97.7M 12s
 51350K .......... .......... .......... .......... .......... 42% 4.84M 12s
 51400K .......... .......... .......... .......... .......... 42%  108M 11s
 51450K .......... .......... .......... .......... .......... 42% 2.91M 12s
 51500K .......... .......... .......... .......... .......... 42%  126M 11s
 51550K .......... .......... .......... .......... .......... 42% 4.82M 11s
 51600K .......... .......... .......... .......... .......... 42% 4.76M 11s
 51650K .......... .......... .......... .......... .......... 42% 4.75M 11s
 51700K .......... .......... .......... .......... .......... 42% 4.84M 11s
 51750K .......... .......... .......... .......... .......... 42% 4.75M 11s
 51800K .......... .......... .......... .......... .......... 42%  122M 11s
 51850K .......... .......... .......... .......... .......... 43% 4.82M 11s
 51900K .......... .......... .......... .......... .......... 43% 4.80M 11s
 51950K .......... .......... .......... .......... .......... 43% 4.78M 11s
 52000K .......... .......... .......... .......... .......... 43%  125M 11s
 52050K .......... .......... .......... .......... .......... 43% 4.75M 11s
 52100K .......... .......... .......... .......... .......... 43% 32.1M 11s
 52150K .......... .......... .......... .......... .......... 43% 3.31M 11s
 52200K .......... .......... .......... .......... .......... 43% 4.80M 11s
 52250K .......... .......... .......... .......... .......... 43% 4.80M 11s
 52300K .......... .......... .......... .......... .......... 43%  101M 11s
 52350K .......... .......... .......... .......... .......... 43% 4.81M 11s
 52400K .......... .......... .......... .......... .......... 43% 4.76M 11s
 52450K .......... .......... .......... .......... .......... 43% 4.76M 11s
 52500K .......... .......... .......... .......... .......... 43% 4.83M 11s
 52550K .......... .......... .......... .......... .......... 43% 4.81M 11s
 52600K .......... .......... .......... .......... .......... 43% 94.6M 11s
 52650K .......... .......... .......... .......... .......... 43% 4.81M 11s
 52700K .......... .......... .......... .......... .......... 43% 4.78M 11s
 52750K .......... .......... .......... .......... .......... 43% 4.76M 11s
 52800K .......... .......... .......... .......... .......... 43%  134M 11s
 52850K .......... .......... .......... .......... .......... 43% 4.75M 11s
 52900K .......... .......... .......... .......... .......... 43% 86.3M 11s
 52950K .......... .......... .......... .......... .......... 43% 3.31M 11s
 53000K .......... .......... .......... .......... .......... 43% 4.75M 11s
 53050K .......... .......... .......... .......... .......... 44% 4.75M 11s
 53100K .......... .......... .......... .......... .......... 44%  128M 11s
 53150K .......... .......... .......... .......... .......... 44% 4.83M 11s
 53200K .......... .......... .......... .......... .......... 44% 4.73M 11s
 53250K .......... .......... .......... .......... .......... 44% 4.77M 11s
 53300K .......... .......... .......... .......... .......... 44% 4.83M 11s
 53350K .......... .......... .......... .......... .......... 44% 4.79M 11s
 53400K .......... .......... .......... .......... .......... 44% 95.7M 11s
 53450K .......... .......... .......... .......... .......... 44% 4.81M 11s
 53500K .......... .......... .......... .......... .......... 44% 4.80M 11s
 53550K .......... .......... .......... .......... .......... 44% 4.77M 11s
 53600K .......... .......... .......... .......... .......... 44%  134M 11s
 53650K .......... .......... .......... .......... .......... 44% 4.76M 11s
 53700K .......... .......... .......... .......... .......... 44% 4.80M 11s
 53750K .......... .......... .......... .......... .......... 44% 4.77M 11s
 53800K .......... .......... .......... .......... .......... 44% 4.83M 11s
 53850K .......... .......... .......... .......... .......... 44% 4.73M 11s
 53900K .......... .......... .......... .......... .......... 44%  121M 11s
 53950K .......... .......... .......... .......... .......... 44%  124M 11s
 54000K .......... .......... .......... .......... .......... 44% 4.20M 11s
 54050K .......... .......... .......... .......... .......... 44% 4.68M 11s
 54100K .......... .......... .......... .......... .......... 44% 4.82M 11s
 54150K .......... .......... .......... .......... .......... 44%  131M 11s
 54200K .......... .......... .......... .......... .......... 44% 4.74M 11s
 54250K .......... .......... .......... .......... .......... 45% 4.82M 11s
 54300K .......... .......... .......... .......... .......... 45% 4.83M 11s
 54350K .......... .......... .......... .......... .......... 45% 4.76M 11s
 54400K .......... .......... .......... .......... .......... 45%  123M 11s
 54450K .......... .......... .......... .......... .......... 45% 3.84M 11s
 54500K .......... .......... .......... .......... .......... 45% 4.56M 11s
 54550K .......... .......... .......... .......... .......... 45% 2.73M 11s
 54600K .......... .......... .......... .......... .......... 45%  206M 11s
 54650K .......... .......... .......... .......... .......... 45% 3.99M 11s
 54700K .......... .......... .......... .......... .......... 45% 97.2M 11s
 54750K .......... .......... .......... .......... .......... 45% 4.81M 11s
 54800K .......... .......... .......... .......... .......... 45% 4.79M 11s
 54850K .......... .......... .......... .......... .......... 45% 4.74M 11s
 54900K .......... .......... .......... .......... .......... 45% 4.79M 11s
 54950K .......... .......... .......... .......... .......... 45%  123M 11s
 55000K .......... .......... .......... .......... .......... 45% 4.81M 11s
 55050K .......... .......... .......... .......... .......... 45% 9.59M 11s
 55100K .......... .......... .......... .......... .......... 45% 3.60M 11s
 55150K .......... .......... .......... .......... .......... 45% 4.73M 11s
 55200K .......... .......... .......... .......... .......... 45%  139M 11s
 55250K .......... .......... .......... .......... .......... 45% 4.75M 11s
 55300K .......... .......... .......... .......... .......... 45% 4.79M 11s
 55350K .......... .......... .......... .......... .......... 45% 4.72M 11s
 55400K .......... .......... .......... .......... .......... 45%  129M 11s
 55450K .......... .......... .......... .......... .......... 45% 6.19M 11s
 55500K .......... .......... .......... .......... .......... 46% 4.59M 11s
 55550K .......... .......... .......... .......... .......... 46% 4.85M 11s
 55600K .......... .......... .......... .......... .......... 46% 4.77M 11s
 55650K .......... .......... .......... .......... .......... 46% 4.76M 11s
 55700K .......... .......... .......... .......... .......... 46% 4.79M 11s
 55750K .......... .......... .......... .......... .......... 46%  129M 11s
 55800K .......... .......... .......... .......... .......... 46% 7.54M 11s
 55850K .......... .......... .......... .......... .......... 46% 4.00M 11s
 55900K .......... .......... .......... .......... .......... 46% 4.83M 11s
 55950K .......... .......... .......... .......... .......... 46% 4.80M 11s
 56000K .......... .......... .......... .......... .......... 46% 98.5M 11s
 56050K .......... .......... .......... .......... .......... 46% 4.75M 11s
 56100K .......... .......... .......... .......... .......... 46% 4.84M 11s
 56150K .......... .......... .......... .......... .......... 46%  107M 11s
 56200K .......... .......... .......... .......... .......... 46% 2.85M 11s
 56250K .......... .......... .......... .......... .......... 46%  116M 11s
 56300K .......... .......... .......... .......... .......... 46% 4.75M 11s
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 70650K .......... .......... .......... .......... .......... 58% 94.9M 8s
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 70750K .......... .......... .......... .......... .......... 58% 4.83M 8s
 70800K .......... .......... .......... .......... .......... 58% 81.6M 8s
 70850K .......... .......... .......... .......... .......... 58% 4.75M 8s
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 71350K .......... .......... .......... .......... .......... 59% 9.88M 8s
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 71900K .......... .......... .......... .......... .......... 59% 4.79M 8s
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 72100K .......... .......... .......... .......... .......... 59%  107M 8s
 72150K .......... .......... .......... .......... .......... 59% 10.3M 8s
 72200K .......... .......... .......... .......... .......... 59% 4.24M 8s
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 72950K .......... .......... .......... .......... .......... 60% 92.7M 8s
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 73050K .......... .......... .......... .......... .......... 60% 3.20M 8s
 73100K .......... .......... .......... .......... .......... 60% 4.61M 8s
 73150K .......... .......... .......... .......... .......... 60%  133M 8s
 73200K .......... .......... .......... .......... .......... 60% 4.80M 8s
 73250K .......... .......... .......... .......... .......... 60% 2.37M 8s
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 73750K .......... .......... .......... .......... .......... 61% 98.1M 8s
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 74050K .......... .......... .......... .......... .......... 61% 3.87M 8s
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 84900K .......... .......... .......... .......... .......... 70% 4.83M 6s
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 85000K .......... .......... .......... .......... .......... 70% 4.78M 6s
 85050K .......... .......... .......... .......... .......... 70%  114M 6s
 85100K .......... .......... .......... .......... .......... 70% 4.80M 6s
 85150K .......... .......... .......... .......... .......... 70% 4.77M 6s
 85200K .......... .......... .......... .......... .......... 70% 4.81M 6s
 85250K .......... .......... .......... .......... .......... 70% 80.3M 6s
 85300K .......... .......... .......... .......... .......... 70% 3.27M 6s
 85350K .......... .......... .......... .......... .......... 70%  125M 6s
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 85600K .......... .......... .......... .......... .......... 70% 2.35M 6s
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 85700K .......... .......... .......... .......... .......... 71% 5.07M 6s
 85750K .......... .......... .......... .......... .......... 71% 3.90M 6s
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 85900K .......... .......... .......... .......... .......... 71% 9.39M 6s
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 86100K .......... .......... .......... .......... .......... 71% 4.77M 6s
 86150K .......... .......... .......... .......... .......... 71%  126M 6s
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 86250K .......... .......... .......... .......... .......... 71% 4.76M 6s
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 86650K .......... .......... .......... .......... .......... 71% 95.6M 6s
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 86900K .......... .......... .......... .......... .......... 72%  110M 6s
 86950K .......... .......... .......... .......... .......... 72% 7.25M 6s
 87000K .......... .......... .......... .......... .......... 72% 4.16M 6s
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 87100K .......... .......... .......... .......... .......... 72% 4.78M 6s
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 88600K .......... .......... .......... .......... .......... 73% 31.3M 5s
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 88700K .......... .......... .......... .......... .......... 73% 4.76M 5s
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 89100K .......... .......... .......... .......... .......... 73% 4.82M 5s
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 91800K .......... .......... .......... .......... .......... 76% 2.36M 5s
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 92150K .......... .......... .......... .......... .......... 76% 4.83M 5s
 92200K .......... .......... .......... .......... .......... 76% 97.1M 5s
 92250K .......... .......... .......... .......... .......... 76% 4.78M 5s
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 94150K .......... .......... .......... .......... .......... 78% 11.5M 4s
 94200K .......... .......... .......... .......... .......... 78% 3.90M 4s
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 95400K .......... .......... .......... .......... .......... 79% 86.6M 4s
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 96700K .......... .......... .......... .......... .......... 80% 98.2M 4s
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 96950K .......... .......... .......... .......... .......... 80%  127M 4s
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 97150K .......... .......... .......... .......... .......... 80%  124M 4s
 97200K .......... .......... .......... .......... .......... 80% 9.15M 4s
 97250K .......... .......... .......... .......... .......... 80% 4.24M 4s
 97300K .......... .......... .......... .......... .......... 80% 4.80M 4s
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 97500K .......... .......... .......... .......... .......... 80% 96.5M 4s
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 97750K .......... .......... .......... .......... .......... 81%  122M 4s
 97800K .......... .......... .......... .......... .......... 81% 4.80M 4s
 97850K .......... .......... .......... .......... .......... 81% 4.77M 4s
 97900K .......... .......... .......... .......... .......... 81%  128M 4s
 97950K .......... .......... .......... .......... .......... 81% 3.09M 4s
 98000K .......... .......... .......... .......... .......... 81%  101M 4s
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 98150K .......... .......... .......... .......... .......... 81% 4.75M 4s
 98200K .......... .......... .......... .......... .......... 81% 4.73M 4s
 98250K .......... .......... .......... .......... .......... 81%  116M 4s
 98300K .......... .......... .......... .......... .......... 81% 4.75M 4s
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 98500K .......... .......... .......... .......... .......... 81% 4.79M 4s
 98550K .......... .......... .......... .......... .......... 81%  125M 4s
 98600K .......... .......... .......... .......... .......... 81% 4.80M 4s
 98650K .......... .......... .......... .......... .......... 81% 4.76M 4s
 98700K .......... .......... .......... .......... .......... 81%  128M 4s
 98750K .......... .......... .......... .......... .......... 81% 3.24M 4s
 98800K .......... .......... .......... .......... .......... 81% 97.4M 4s
 98850K .......... .......... .......... .......... .......... 81% 4.73M 4s
 98900K .......... .......... .......... .......... .......... 82% 4.82M 4s
 98950K .......... .......... .......... .......... .......... 82% 2.38M 4s
 99000K .......... .......... .......... .......... .......... 82%  190M 4s
 99050K .......... .......... .......... .......... .......... 82% 7.13M 4s
 99100K .......... .......... .......... .......... .......... 82% 8.01M 4s
 99150K .......... .......... .......... .......... .......... 82% 11.6M 4s
 99200K .......... .......... .......... .......... .......... 82% 3.82M 4s
 99250K .......... .......... .......... .......... .......... 82% 4.71M 4s
 99300K .......... .......... .......... .......... .......... 82% 4.78M 4s
 99350K .......... .......... .......... .......... .......... 82%  124M 3s
 99400K .......... .......... .......... .......... .......... 82% 4.75M 3s
 99450K .......... .......... .......... .......... .......... 82% 4.76M 3s
 99500K .......... .......... .......... .......... .......... 82% 4.82M 3s
 99550K .......... .......... .......... .......... .......... 82%  113M 3s
 99600K .......... .......... .......... .......... .......... 82% 4.76M 3s
 99650K .......... .......... .......... .......... .......... 82% 4.68M 3s
 99700K .......... .......... .......... .......... .......... 82% 4.79M 3s
 99750K .......... .......... .......... .......... .......... 82% 28.4M 3s
 99800K .......... .......... .......... .......... .......... 82% 2.84M 3s
 99850K .......... .......... .......... .......... .......... 82%  119M 3s
 99900K .......... .......... .......... .......... .......... 82% 4.81M 3s
 99950K .......... .......... .......... .......... .......... 82% 4.77M 3s
100000K .......... .......... .......... .......... .......... 82% 4.82M 3s
100050K .......... .......... .......... .......... .......... 82% 87.5M 3s
100100K .......... .......... .......... .......... .......... 83% 2.68M 3s
100150K .......... .......... .......... .......... .......... 83% 98.2M 3s
100200K .......... .......... .......... .......... .......... 83% 4.82M 3s
100250K .......... .......... .......... .......... .......... 83% 4.73M 3s
100300K .......... .......... .......... .......... .......... 83% 4.83M 3s
100350K .......... .......... .......... .......... .......... 83%  132M 3s
100400K .......... .......... .......... .......... .......... 83% 7.51M 3s
100450K .......... .......... .......... .......... .......... 83% 4.08M 3s
100500K .......... .......... .......... .......... .......... 83% 4.84M 3s
100550K .......... .......... .......... .......... .......... 83% 4.78M 3s
100600K .......... .......... .......... .......... .......... 83% 4.81M 3s
100650K .......... .......... .......... .......... .......... 83%  116M 3s
100700K .......... .......... .......... .......... .......... 83% 4.80M 3s
100750K .......... .......... .......... .......... .......... 83% 4.78M 3s
100800K .......... .......... .......... .......... .......... 83% 4.80M 3s
100850K .......... .......... .......... .......... .......... 83% 92.6M 3s
100900K .......... .......... .......... .......... .......... 83% 2.86M 3s
100950K .......... .......... .......... .......... .......... 83% 99.8M 3s
101000K .......... .......... .......... .......... .......... 83% 4.85M 3s
101050K .......... .......... .......... .......... .......... 83% 4.80M 3s
101100K .......... .......... .......... .......... .......... 83% 4.75M 3s
101150K .......... .......... .......... .......... .......... 83%  134M 3s
101200K .......... .......... .......... .......... .......... 83% 4.80M 3s
101250K .......... .......... .......... .......... .......... 83% 4.70M 3s
101300K .......... .......... .......... .......... .......... 83% 4.82M 3s
101350K .......... .......... .......... .......... .......... 84% 4.82M 3s
101400K .......... .......... .......... .......... .......... 84%  109M 3s
101450K .......... .......... .......... .......... .......... 84% 7.93M 3s
101500K .......... .......... .......... .......... .......... 84% 1.55M 3s
101550K .......... .......... .......... .......... .......... 84%  208M 3s
101600K .......... .......... .......... .......... .......... 84%  219M 3s
101650K .......... .......... .......... .......... .......... 84% 6.08M 3s
101700K .......... .......... .......... .......... .......... 84% 8.96M 3s
101750K .......... .......... .......... .......... .......... 84% 11.6M 3s
101800K .......... .......... .......... .......... .......... 84% 4.28M 3s
101850K .......... .......... .......... .......... .......... 84% 4.79M 3s
101900K .......... .......... .......... .......... .......... 84% 4.44M 3s
101950K .......... .......... .......... .......... .......... 84%  134M 3s
102000K .......... .......... .......... .......... .......... 84% 4.80M 3s
102050K .......... .......... .......... .......... .......... 84% 5.71M 3s
102100K .......... .......... .......... .......... .......... 84% 10.6M 3s
102150K .......... .......... .......... .......... .......... 84% 3.74M 3s
102200K .......... .......... .......... .......... .......... 84% 4.72M 3s
102250K .......... .......... .......... .......... .......... 84%  120M 3s
102300K .......... .......... .......... .......... .......... 84% 4.57M 3s
102350K .......... .......... .......... .......... .......... 84% 4.66M 3s
102400K .......... .......... .......... .......... .......... 84% 4.79M 3s
102450K .......... .......... .......... .......... .......... 84% 4.77M 3s
102500K .......... .......... .......... .......... .......... 84%  129M 3s
102550K .......... .......... .......... .......... .......... 85% 4.62M 3s
102600K .......... .......... .......... .......... .......... 85% 4.73M 3s
102650K .......... .......... .......... .......... .......... 85% 4.73M 3s
102700K .......... .......... .......... .......... .......... 85% 46.8M 3s
102750K .......... .......... .......... .......... .......... 85% 6.06M 3s
102800K .......... .......... .......... .......... .......... 85% 4.38M 3s
102850K .......... .......... .......... .......... .......... 85% 4.77M 3s
102900K .......... .......... .......... .......... .......... 85% 8.23M 3s
102950K .......... .......... .......... .......... .......... 85% 9.08M 3s
103000K .......... .......... .......... .......... .......... 85% 3.46M 3s
103050K .......... .......... .......... .......... .......... 85% 81.9M 3s
103100K .......... .......... .......... .......... .......... 85% 4.82M 3s
103150K .......... .......... .......... .......... .......... 85% 4.78M 3s
103200K .......... .......... .......... .......... .......... 85% 4.78M 3s
103250K .......... .......... .......... .......... .......... 85% 4.77M 3s
103300K .......... .......... .......... .......... .......... 85%  125M 3s
103350K .......... .......... .......... .......... .......... 85% 4.76M 3s
103400K .......... .......... .......... .......... .......... 85% 4.86M 3s
103450K .......... .......... .......... .......... .......... 85% 4.79M 3s
103500K .......... .......... .......... .......... .......... 85% 4.74M 3s
103550K .......... .......... .......... .......... .......... 85%  136M 3s
103600K .......... .......... .......... .......... .......... 85% 4.84M 3s
103650K .......... .......... .......... .......... .......... 85% 4.72M 3s
103700K .......... .......... .......... .......... .......... 85% 4.79M 3s
103750K .......... .......... .......... .......... .......... 86% 4.85M 3s
103800K .......... .......... .......... .......... .......... 86% 4.81M 3s
103850K .......... .......... .......... .......... .......... 86% 87.5M 3s
103900K .......... .......... .......... .......... .......... 86% 4.84M 3s
103950K .......... .......... .......... .......... .......... 86% 4.81M 3s
104000K .......... .......... .......... .......... .......... 86% 94.4M 3s
104050K .......... .......... .......... .......... .......... 86% 3.49M 3s
104100K .......... .......... .......... .......... .......... 86% 10.6M 3s
104150K .......... .......... .......... .......... .......... 86% 4.44M 3s
104200K .......... .......... .......... .......... .......... 86% 8.94M 3s
104250K .......... .......... .......... .......... .......... 86% 3.62M 3s
104300K .......... .......... .......... .......... .......... 86% 4.83M 3s
104350K .......... .......... .......... .......... .......... 86% 89.4M 3s
104400K .......... .......... .......... .......... .......... 86% 4.83M 3s
104450K .......... .......... .......... .......... .......... 86% 4.76M 3s
104500K .......... .......... .......... .......... .......... 86% 52.0M 3s
104550K .......... .......... .......... .......... .......... 86% 2.78M 3s
104600K .......... .......... .......... .......... .......... 86%  124M 3s
104650K .......... .......... .......... .......... .......... 86% 4.73M 3s
104700K .......... .......... .......... .......... .......... 86% 4.80M 3s
104750K .......... .......... .......... .......... .......... 86% 4.81M 3s
104800K .......... .......... .......... .......... .......... 86% 4.77M 3s
104850K .......... .......... .......... .......... .......... 86% 93.6M 3s
104900K .......... .......... .......... .......... .......... 86% 6.44M 3s
104950K .......... .......... .......... .......... .......... 87% 4.34M 3s
105000K .......... .......... .......... .......... .......... 87% 4.78M 3s
105050K .......... .......... .......... .......... .......... 87% 4.82M 3s
105100K .......... .......... .......... .......... .......... 87% 4.80M 3s
105150K .......... .......... .......... .......... .......... 87% 98.1M 3s
105200K .......... .......... .......... .......... .......... 87% 10.2M 3s
105250K .......... .......... .......... .......... .......... 87% 3.72M 3s
105300K .......... .......... .......... .......... .......... 87% 4.80M 3s
105350K .......... .......... .......... .......... .......... 87% 4.76M 3s
105400K .......... .......... .......... .......... .......... 87%  128M 2s
105450K .......... .......... .......... .......... .......... 87% 4.78M 2s
105500K .......... .......... .......... .......... .......... 87% 4.78M 2s
105550K .......... .......... .......... .......... .......... 87% 4.83M 2s
105600K .......... .......... .......... .......... .......... 87% 4.74M 2s
105650K .......... .......... .......... .......... .......... 87% 87.9M 2s
105700K .......... .......... .......... .......... .......... 87% 8.17M 2s
105750K .......... .......... .......... .......... .......... 87% 4.35M 2s
105800K .......... .......... .......... .......... .......... 87% 4.74M 2s
105850K .......... .......... .......... .......... .......... 87% 4.81M 2s
105900K .......... .......... .......... .......... .......... 87%  136M 2s
105950K .......... .......... .......... .......... .......... 87% 4.75M 2s
106000K .......... .......... .......... .......... .......... 87% 4.84M 2s
106050K .......... .......... .......... .......... .......... 87% 4.75M 2s
106100K .......... .......... .......... .......... .......... 87% 4.77M 2s
106150K .......... .......... .......... .......... .......... 88% 4.80M 2s
106200K .......... .......... .......... .......... .......... 88%  125M 2s
106250K .......... .......... .......... .......... .......... 88% 4.79M 2s
106300K .......... .......... .......... .......... .......... 88% 98.3M 2s
106350K .......... .......... .......... .......... .......... 88% 3.01M 2s
106400K .......... .......... .......... .......... .......... 88% 4.76M 2s
106450K .......... .......... .......... .......... .......... 88% 4.74M 2s
106500K .......... .......... .......... .......... .......... 88%  128M 2s
106550K .......... .......... .......... .......... .......... 88% 4.84M 2s
106600K .......... .......... .......... .......... .......... 88% 4.74M 2s
106650K .......... .......... .......... .......... .......... 88% 4.80M 2s
106700K .......... .......... .......... .......... .......... 88%  134M 2s
106750K .......... .......... .......... .......... .......... 88% 4.74M 2s
106800K .......... .......... .......... .......... .......... 88% 4.86M 2s
106850K .......... .......... .......... .......... .......... 88% 4.73M 2s
106900K .......... .......... .......... .......... .......... 88% 4.78M 2s
106950K .......... .......... .......... .......... .......... 88% 4.79M 2s
107000K .......... .......... .......... .......... .......... 88%  125M 2s
107050K .......... .......... .......... .......... .......... 88% 4.79M 2s
107100K .......... .......... .......... .......... .......... 88%  125M 2s
107150K .......... .......... .......... .......... .......... 88% 3.02M 2s
107200K .......... .......... .......... .......... .......... 88% 4.72M 2s
107250K .......... .......... .......... .......... .......... 88% 4.70M 2s
107300K .......... .......... .......... .......... .......... 88%  124M 2s
107350K .......... .......... .......... .......... .......... 89% 4.78M 2s
107400K .......... .......... .......... .......... .......... 89% 4.79M 2s
107450K .......... .......... .......... .......... .......... 89% 4.76M 2s
107500K .......... .......... .......... .......... .......... 89%  123M 2s
107550K .......... .......... .......... .......... .......... 89% 4.80M 2s
107600K .......... .......... .......... .......... .......... 89% 4.76M 2s
107650K .......... .......... .......... .......... .......... 89% 4.73M 2s
107700K .......... .......... .......... .......... .......... 89% 4.83M 2s
107750K .......... .......... .......... .......... .......... 89%  105M 2s
107800K .......... .......... .......... .......... .......... 89% 26.8M 2s
107850K .......... .......... .......... .......... .......... 89% 3.46M 2s
107900K .......... .......... .......... .......... .......... 89% 4.76M 2s
107950K .......... .......... .......... .......... .......... 89% 4.84M 2s
108000K .......... .......... .......... .......... .......... 89%  131M 2s
108050K .......... .......... .......... .......... .......... 89% 2.43M 2s
108100K .......... .......... .......... .......... .......... 89% 92.3M 2s
108150K .......... .......... .......... .......... .......... 89% 4.84M 2s
108200K .......... .......... .......... .......... .......... 89% 4.81M 2s
108250K .......... .......... .......... .......... .......... 89% 4.74M 2s
108300K .......... .......... .......... .......... .......... 89%  132M 2s
108350K .......... .......... .......... .......... .......... 89% 4.79M 2s
108400K .......... .......... .......... .......... .......... 89% 4.77M 2s
108450K .......... .......... .......... .......... .......... 89% 4.75M 2s
108500K .......... .......... .......... .......... .......... 89% 4.82M 2s
108550K .......... .......... .......... .......... .......... 90% 4.75M 2s
108600K .......... .......... .......... .......... .......... 90%  123M 2s
108650K .......... .......... .......... .......... .......... 90% 4.81M 2s
108700K .......... .......... .......... .......... .......... 90%  111M 2s
108750K .......... .......... .......... .......... .......... 90% 3.12M 2s
108800K .......... .......... .......... .......... .......... 90%  126M 2s
108850K .......... .......... .......... .......... .......... 90% 4.65M 2s
108900K .......... .......... .......... .......... .......... 90% 4.75M 2s
108950K .......... .......... .......... .......... .......... 90% 4.82M 2s
109000K .......... .......... .......... .......... .......... 90% 4.80M 2s
109050K .......... .......... .......... .......... .......... 90% 4.76M 2s
109100K .......... .......... .......... .......... .......... 90%  134M 2s
109150K .......... .......... .......... .......... .......... 90% 4.83M 2s
109200K .......... .......... .......... .......... .......... 90% 4.76M 2s
109250K .......... .......... .......... .......... .......... 90% 4.75M 2s
109300K .......... .......... .......... .......... .......... 90% 4.82M 2s
109350K .......... .......... .......... .......... .......... 90% 4.80M 2s
109400K .......... .......... .......... .......... .......... 90% 97.9M 2s
109450K .......... .......... .......... .......... .......... 90% 4.81M 2s
109500K .......... .......... .......... .......... .......... 90%  110M 2s
109550K .......... .......... .......... .......... .......... 90% 3.74M 2s
109600K .......... .......... .......... .......... .......... 90%  133M 2s
109650K .......... .......... .......... .......... .......... 90% 4.35M 2s
109700K .......... .......... .......... .......... .......... 90% 4.72M 2s
109750K .......... .......... .......... .......... .......... 91% 2.37M 2s
109800K .......... .......... .......... .......... .......... 91%  206M 2s
109850K .......... .......... .......... .......... .......... 91% 7.32M 2s
109900K .......... .......... .......... .......... .......... 91% 8.84M 2s
109950K .......... .......... .......... .......... .......... 91% 4.34M 2s
110000K .......... .......... .......... .......... .......... 91% 4.76M 2s
110050K .......... .......... .......... .......... .......... 91% 9.53M 2s
110100K .......... .......... .......... .......... .......... 91% 3.41M 2s
110150K .......... .......... .......... .......... .......... 91% 4.84M 2s
110200K .......... .......... .......... .......... .......... 91% 85.3M 2s
110250K .......... .......... .......... .......... .......... 91% 4.80M 2s
110300K .......... .......... .......... .......... .......... 91% 29.2M 2s
110350K .......... .......... .......... .......... .......... 91% 2.89M 2s
110400K .......... .......... .......... .......... .......... 91%  125M 2s
110450K .......... .......... .......... .......... .......... 91% 4.76M 2s
110500K .......... .......... .......... .......... .......... 91% 4.80M 2s
110550K .......... .......... .......... .......... .......... 91% 4.79M 2s
110600K .......... .......... .......... .......... .......... 91% 4.85M 2s
110650K .......... .......... .......... .......... .......... 91%  102M 2s
110700K .......... .......... .......... .......... .......... 91% 7.04M 2s
110750K .......... .......... .......... .......... .......... 91% 4.24M 2s
110800K .......... .......... .......... .......... .......... 91% 4.61M 2s
110850K .......... .......... .......... .......... .......... 91% 4.93M 2s
110900K .......... .......... .......... .......... .......... 91% 8.20M 2s
110950K .......... .......... .......... .......... .......... 91% 8.31M 2s
111000K .......... .......... .......... .......... .......... 92% 11.0M 2s
111050K .......... .......... .......... .......... .......... 92% 3.74M 2s
111100K .......... .......... .......... .......... .......... 92% 4.85M 2s
111150K .......... .......... .......... .......... .......... 92% 9.67M 2s
111200K .......... .......... .......... .......... .......... 92% 7.77M 2s
111250K .......... .......... .......... .......... .......... 92% 7.98M 2s
111300K .......... .......... .......... .......... .......... 92% 4.40M 2s
111350K .......... .......... .......... .......... .......... 92% 6.24M 2s
111400K .......... .......... .......... .......... .......... 92% 3.35M 2s
111450K .......... .......... .......... .......... .......... 92% 4.82M 2s
111500K .......... .......... .......... .......... .......... 92% 7.94M 1s
111550K .......... .......... .......... .......... .......... 92% 9.07M 1s
111600K .......... .......... .......... .......... .......... 92% 11.0M 1s
111650K .......... .......... .......... .......... .......... 92% 3.52M 1s
111700K .......... .......... .......... .......... .......... 92% 4.78M 1s
111750K .......... .......... .......... .......... .......... 92%  130M 1s
111800K .......... .......... .......... .......... .......... 92% 5.18M 1s
111850K .......... .......... .......... .......... .......... 92% 9.37M 1s
111900K .......... .......... .......... .......... .......... 92% 3.75M 1s
111950K .......... .......... .......... .......... .......... 92% 4.74M 1s
112000K .......... .......... .......... .......... .......... 92%  136M 1s
112050K .......... .......... .......... .......... .......... 92% 4.67M 1s
112100K .......... .......... .......... .......... .......... 92% 5.89M 1s
112150K .......... .......... .......... .......... .......... 92% 3.84M 1s
112200K .......... .......... .......... .......... .......... 93% 9.97M 1s
112250K .......... .......... .......... .......... .......... 93% 8.15M 1s
112300K .......... .......... .......... .......... .......... 93% 10.6M 1s
112350K .......... .......... .......... .......... .......... 93% 4.20M 1s
112400K .......... .......... .......... .......... .......... 93% 4.78M 1s
112450K .......... .......... .......... .......... .......... 93% 4.52M 1s
112500K .......... .......... .......... .......... .......... 93% 4.48M 1s
112550K .......... .......... .......... .......... .......... 93%  126M 1s
112600K .......... .......... .......... .......... .......... 93% 4.75M 1s
112650K .......... .......... .......... .......... .......... 93% 4.67M 1s
112700K .......... .......... .......... .......... .......... 93% 25.6M 1s
112750K .......... .......... .......... .......... .......... 93% 2.80M 1s
112800K .......... .......... .......... .......... .......... 93% 85.5M 1s
112850K .......... .......... .......... .......... .......... 93% 4.36M 1s
112900K .......... .......... .......... .......... .......... 93% 5.19M 1s
112950K .......... .......... .......... .......... .......... 93% 9.10M 1s
113000K .......... .......... .......... .......... .......... 93% 9.38M 1s
113050K .......... .......... .......... .......... .......... 93% 5.19M 1s
113100K .......... .......... .......... .......... .......... 93% 10.2M 1s
113150K .......... .......... .......... .......... .......... 93% 4.02M 1s
113200K .......... .......... .......... .......... .......... 93% 4.84M 1s
113250K .......... .......... .......... .......... .......... 93% 4.72M 1s
113300K .......... .......... .......... .......... .......... 93% 4.78M 1s
113350K .......... .......... .......... .......... .......... 93%  129M 1s
113400K .......... .......... .......... .......... .......... 94% 4.82M 1s
113450K .......... .......... .......... .......... .......... 94% 4.75M 1s
113500K .......... .......... .......... .......... .......... 94% 4.70M 1s
113550K .......... .......... .......... .......... .......... 94% 4.81M 1s
113600K .......... .......... .......... .......... .......... 94% 99.1M 1s
113650K .......... .......... .......... .......... .......... 94% 4.75M 1s
113700K .......... .......... .......... .......... .......... 94% 8.93M 1s
113750K .......... .......... .......... .......... .......... 94% 3.73M 1s
113800K .......... .......... .......... .......... .......... 94% 4.77M 1s
113850K .......... .......... .......... .......... .......... 94%  120M 1s
113900K .......... .......... .......... .......... .......... 94% 4.80M 1s
113950K .......... .......... .......... .......... .......... 94% 4.67M 1s
114000K .......... .......... .......... .......... .......... 94%  131M 1s
114050K .......... .......... .......... .......... .......... 94% 2.70M 1s
114100K .......... .......... .......... .......... .......... 94% 4.75M 1s
114150K .......... .......... .......... .......... .......... 94%  129M 1s
114200K .......... .......... .......... .......... .......... 94% 4.82M 1s
114250K .......... .......... .......... .......... .......... 94% 4.74M 1s
114300K .......... .......... .......... .......... .......... 94% 4.82M 1s
114350K .......... .......... .......... .......... .......... 94%  136M 1s
114400K .......... .......... .......... .......... .......... 94% 7.09M 1s
114450K .......... .......... .......... .......... .......... 94% 3.99M 1s
114500K .......... .......... .......... .......... .......... 94% 4.83M 1s
114550K .......... .......... .......... .......... .......... 94% 4.81M 1s
114600K .......... .......... .......... .......... .......... 95% 4.79M 1s
114650K .......... .......... .......... .......... .......... 95%  116M 1s
114700K .......... .......... .......... .......... .......... 95% 9.15M 1s
114750K .......... .......... .......... .......... .......... 95% 3.93M 1s
114800K .......... .......... .......... .......... .......... 95% 4.83M 1s
114850K .......... .......... .......... .......... .......... 95% 4.76M 1s
114900K .......... .......... .......... .......... .......... 95% 4.73M 1s
114950K .......... .......... .......... .......... .......... 95%  128M 1s
115000K .......... .......... .......... .......... .......... 95% 4.85M 1s
115050K .......... .......... .......... .......... .......... 95% 4.77M 1s
115100K .......... .......... .......... .......... .......... 95% 4.77M 1s
115150K .......... .......... .......... .......... .......... 95%  136M 1s
115200K .......... .......... .......... .......... .......... 95% 4.80M 1s
115250K .......... .......... .......... .......... .......... 95% 7.38M 1s
115300K .......... .......... .......... .......... .......... 95% 4.08M 1s
115350K .......... .......... .......... .......... .......... 95% 4.82M 1s
115400K .......... .......... .......... .......... .......... 95% 4.76M 1s
115450K .......... .......... .......... .......... .......... 95%  115M 1s
115500K .......... .......... .......... .......... .......... 95% 4.83M 1s
115550K .......... .......... .......... .......... .......... 95% 4.74M 1s
115600K .......... .......... .......... .......... .......... 95% 4.83M 1s
115650K .......... .......... .......... .......... .......... 95% 93.3M 1s
115700K .......... .......... .......... .......... .......... 95% 2.85M 1s
115750K .......... .......... .......... .......... .......... 95% 86.9M 1s
115800K .......... .......... .......... .......... .......... 96% 4.83M 1s
115850K .......... .......... .......... .......... .......... 96% 4.79M 1s
115900K .......... .......... .......... .......... .......... 96% 4.74M 1s
115950K .......... .......... .......... .......... .......... 96%  134M 1s
116000K .......... .......... .......... .......... .......... 96% 4.80M 1s
116050K .......... .......... .......... .......... .......... 96% 4.70M 1s
116100K .......... .......... .......... .......... .......... 96% 4.84M 1s
116150K .......... .......... .......... .......... .......... 96% 4.83M 1s
116200K .......... .......... .......... .......... .......... 96%  110M 1s
116250K .......... .......... .......... .......... .......... 96% 8.00M 1s
116300K .......... .......... .......... .......... .......... 96% 4.11M 1s
116350K .......... .......... .......... .......... .......... 96% 4.74M 1s
116400K .......... .......... .......... .......... .......... 96% 4.83M 1s
116450K .......... .......... .......... .......... .......... 96% 4.76M 1s
116500K .......... .......... .......... .......... .......... 96% 4.84M 1s
116550K .......... .......... .......... .......... .......... 96% 82.4M 1s
116600K .......... .......... .......... .......... .......... 96% 4.83M 1s
116650K .......... .......... .......... .......... .......... 96% 4.76M 1s
116700K .......... .......... .......... .......... .......... 96%  103M 1s
116750K .......... .......... .......... .......... .......... 96% 8.23M 1s
116800K .......... .......... .......... .......... .......... 96% 4.35M 1s
116850K .......... .......... .......... .......... .......... 96% 4.76M 1s
116900K .......... .......... .......... .......... .......... 96% 4.77M 1s
116950K .......... .......... .......... .......... .......... 96% 4.77M 1s
117000K .......... .......... .......... .......... .......... 97% 4.74M 1s
117050K .......... .......... .......... .......... .......... 97%  114M 1s
117100K .......... .......... .......... .......... .......... 97% 4.84M 1s
117150K .......... .......... .......... .......... .......... 97% 4.75M 1s
117200K .......... .......... .......... .......... .......... 97% 4.81M 1s
117250K .......... .......... .......... .......... .......... 97% 89.2M 1s
117300K .......... .......... .......... .......... .......... 97% 8.60M 1s
117350K .......... .......... .......... .......... .......... 97% 4.54M 1s
117400K .......... .......... .......... .......... .......... 97% 4.85M 1s
117450K .......... .......... .......... .......... .......... 97% 4.83M 1s
117500K .......... .......... .......... .......... .......... 97% 4.73M 1s
117550K .......... .......... .......... .......... .......... 97%  131M 1s
117600K .......... .......... .......... .......... .......... 97% 4.82M 0s
117650K .......... .......... .......... .......... .......... 97% 4.71M 0s
117700K .......... .......... .......... .......... .......... 97% 4.65M 0s
117750K .......... .......... .......... .......... .......... 97% 4.82M 0s
117800K .......... .......... .......... .......... .......... 97% 4.79M 0s
117850K .......... .......... .......... .......... .......... 97% 91.7M 0s
117900K .......... .......... .......... .......... .......... 97% 4.83M 0s
117950K .......... .......... .......... .......... .......... 97% 4.79M 0s
118000K .......... .......... .......... .......... .......... 97% 4.75M 0s
118050K .......... .......... .......... .......... .......... 97% 94.9M 0s
118100K .......... .......... .......... .......... .......... 97% 9.80M 0s
118150K .......... .......... .......... .......... .......... 97% 4.40M 0s
118200K .......... .......... .......... .......... .......... 98% 4.76M 0s
118250K .......... .......... .......... .......... .......... 98% 4.81M 0s
118300K .......... .......... .......... .......... .......... 98% 4.74M 0s
118350K .......... .......... .......... .......... .......... 98%  123M 0s
118400K .......... .......... .......... .......... .......... 98% 4.83M 0s
118450K .......... .......... .......... .......... .......... 98% 4.76M 0s
118500K .......... .......... .......... .......... .......... 98% 4.73M 0s
118550K .......... .......... .......... .......... .......... 98% 4.83M 0s
118600K .......... .......... .......... .......... .......... 98%  125M 0s
118650K .......... .......... .......... .......... .......... 98% 4.73M 0s
118700K .......... .......... .......... .......... .......... 98% 4.83M 0s
118750K .......... .......... .......... .......... .......... 98% 4.80M 0s
118800K .......... .......... .......... .......... .......... 98% 4.64M 0s
118850K .......... .......... .......... .......... .......... 98% 93.8M 0s
118900K .......... .......... .......... .......... .......... 98% 85.9M 0s
118950K .......... .......... .......... .......... .......... 98% 3.27M 0s
119000K .......... .......... .......... .......... .......... 98% 4.77M 0s
119050K .......... .......... .......... .......... .......... 98% 4.80M 0s
119100K .......... .......... .......... .......... .......... 98% 4.80M 0s
119150K .......... .......... .......... .......... .......... 98%  103M 0s
119200K .......... .......... .......... .......... .......... 98% 4.82M 0s
119250K .......... .......... .......... .......... .......... 98% 4.74M 0s
119300K .......... .......... .......... .......... .......... 98% 4.75M 0s
119350K .......... .......... .......... .......... .......... 98% 4.85M 0s
119400K .......... .......... .......... .......... .......... 99%  127M 0s
119450K .......... .......... .......... .......... .......... 99% 4.72M 0s
119500K .......... .......... .......... .......... .......... 99% 4.83M 0s
119550K .......... .......... .......... .......... .......... 99% 4.85M 0s
119600K .......... .......... .......... .......... .......... 99% 4.72M 0s
119650K .......... .......... .......... .......... .......... 99% 4.64M 0s
119700K .......... .......... .......... .......... .......... 99%  125M 0s
119750K .......... .......... .......... .......... .......... 99% 4.80M 0s
119800K .......... .......... .......... .......... .......... 99%  128M 0s
119850K .......... .......... .......... .......... .......... 99% 3.28M 0s
119900K .......... .......... .......... .......... .......... 99% 4.72M 0s
119950K .......... .......... .......... .......... .......... 99% 97.0M 0s
120000K .......... .......... .......... .......... .......... 99% 4.82M 0s
120050K .......... .......... .......... .......... .......... 99% 2.35M 0s
120100K .......... .......... .......... .......... .......... 99%  221M 0s
120150K .......... .......... .......... .......... .......... 99% 5.76M 0s
120200K .......... .......... .......... .......... .......... 99% 9.78M 0s
120250K .......... .......... .......... .......... .......... 99% 11.5M 0s
120300K .......... .......... .......... .......... .......... 99% 4.06M 0s
120350K .......... .......... .......... .......... .......... 99% 4.83M 0s
120400K .......... .......... .......... .......... .......... 99% 4.81M 0s
120450K .......... .......... .......... .......... .......... 99% 4.70M 0s
120500K .......... .......... .......... .......... .......... 99%  124M 0s
120550K .......... .......... .......... .......... .......... 99% 4.85M 0s
120600K .......... .......... .......... .......... .......... 99% 4.80M 0s
120650K ....                                                  100%  124M=20s

2016-11-12 16:35:45 (5.96 MB/s) - ‘traffic-sign-data.zip’ saved [123550596/123550596]

%%bash

mkdir -p data
mv -f traffic-sign-data.zip data/
cd data/
unzip traffic-sign-data.zip

Archive:  traffic-sign-data.zip
  inflating: lab 2 data/changelog.docx  
  inflating: lab 2 data/test.p       
  inflating: lab 2 data/train.p      

# Load pickled data
import pickle

# TODO: fill this in based on where you saved the training and testing data
data_path = "data/lab 2 data"
training_file = data_path + "/train.p"
testing_file = data_path + "/test.p"

with open(training_file, mode='rb') as f:
    train = pickle.load(f)

with open(testing_file, mode='rb') as f:
    test = pickle.load(f)

X_train, y_train = train['features'], train['labels']
X_test, y_test = test['features'], test['labels']

### To start off let's do a basic data summary.

# TODO: number of training examples
n_train = len(X_train) # X_train.shape[0] also works in this case

# TODO: number of testing examples
n_test = len(X_test)

# TODO: what's the shape of an image?
image_shape = X_train[0].shape

# TODO: how many classes are in the dataset
import numpy as np

n_classes = np.unique(y_train).size

print("Number of training examples =", n_train)
print("Number of testing examples =", n_test)
print("Image data shape =", image_shape)
print("Number of classes =", n_classes)

Number of training examples = 39209
Number of testing examples = 12630
Image data shape = (32, 32, 3)
Number of classes = 43

Data exploration visualization goes here.

Feel free to use as many code cells as needed.

Class Imbalance?

Counts of training examples for each of the 43 types of traffic signs:

import pandas as pd
import matplotlib.pyplot as plt
label_series = pd.Series(y_train)
%matplotlib inline
label_series.hist(bins=n_classes)

<matplotlib.axes._subplots.AxesSubplot at 0x7fb52e7fdb70>

View examples for each type of sign:

f = plt.figure(figsize=(30,30))
#plt.title("Example Signs of Each Type")
signnames_dataframe = pd.read_csv('signnames.csv')
for sign_id in range(n_classes):
    training_image_index = next(i for i, label in enumerate(y_train) if label == sign_id)
    sp = f.add_subplot(7, 7, sign_id+1)
    plt.imshow(X_train[training_image_index])
    plt.title("{}: {}".format(sign_id, signnames_dataframe.SignName[sign_id]))
    plt.tight_layout()

---

Step 2: Design and Test a Model Architecture

Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.

There are various aspects to consider when thinking about this problem:

• Your model can be derived from a deep feedforward net or a deep convolutional network.
• Play around preprocessing techniques (normalization, rgb to grayscale, etc)
• Number of examples per label (some have more than others).
• Generate fake data.

Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.

Implementation

Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow.

### Preprocess the data here.
### Feel free to use as many code cells as needed.
from sklearn.preprocessing import StandardScaler #, label_binarize

X_train_reshaped = np.reshape(X_train.astype('float32'), (len(X_train), -1))
X_test_reshaped = np.reshape(X_test.astype('float32'), (len(X_test), -1))
standard_scaler = StandardScaler().fit(X_train_reshaped)
X_train_reshaped_normalized = standard_scaler.transform(X_train_reshaped)
X_test_reshaped_normalized = standard_scaler.transform(X_test_reshaped)
X_train_normalized = X_train_reshaped_normalized.reshape(-1, image_shape[0], image_shape[1], image_shape[2])
X_test_normalized = X_test_reshaped_normalized.reshape(-1, image_shape[0], image_shape[1], image_shape[2])
#classes = range(n_classes)
#y_train_encoded = label_binarize(y_train, classes).astype(np.float32)
#y_test_encoded = label_binarize(y_test, classes).astype(np.float32)

Question 1

Describe the techniques used to preprocess the data.

Answer:

Each image is normalized to be zero-centered and have unit variance. As Vincent Vanhoucke stated in the lesson, one reason for normalization is to enable gradient descent to perform better.

Target labels will be one-hot encoded before calculating cross entropy loss & accuracy (see further down below)...

### Generate data additional (if you want to!)
### and split the data into training/validation/testing sets here.
### Feel free to use as many code cells as needed.
from sklearn import metrics
from sklearn.model_selection import StratifiedKFold, train_test_split

k = 10
training_validation_data_splitter = StratifiedKFold(n_splits=k)
folds = k * [None]
fold_number = 0
for training_indices, validation_indices in training_validation_data_splitter.split(X_train_normalized, y_train):
    folds[fold_number] = { "training_indices": training_indices, "validation_indices": validation_indices }
    fold_number += 1

X_training, X_validation, y_training, y_validation = train_test_split(X_train_normalized, y_train, test_size=0.2)

Question 2

Describe how you set up the training, validation and testing data for your model. If you generated additional data, why?

Answer:

• Using SciKit Learn's test_train_split(), the training images are split into validation and training subsets in which 20% (note the test_size=0.2 parameter) are randomly chosen to be the validation subset. Having a validation subset is a machine learning best practice that allows us to adjust our model and estimate its ability to generalize to real-world examples without needing to cheat and peak at the test set. Setting it at 20% generated a large enough validation set to run a meaningful test of the model, but not too large to take away from having enough training examples for the model to learn from.
• Although not currently used, in order to preserve the original percentages of samples for each traffic sign, StratifiedKFold splits the original X_train training data into a training set & validation set 10 different ways (i.e., 10 different folds).

### Define your architecture here.
### Feel free to use as many code cells as needed.

import tensorflow as tf
import tensorflow.contrib.slim as slim

images = tf.placeholder(tf.float32, shape=(None, image_shape[0], image_shape[1], image_shape[2]))
labels = tf.placeholder(tf.int32)
is_training = tf.placeholder(tf.bool)

def max_pooled_conv(net, scope=None):
    net = slim.conv2d(net, 64, 5, normalizer_fn=slim.batch_norm)
    net = slim.max_pool2d(net, 2)
    return net

def model(images, num_classes, is_training=False, dropout_keep_prob=0.71875, prediction_fn=slim.softmax, scope='CifarNet'):
    # https://github.com/tensorflow/models/blob/master/slim/nets/cifarnet.py
    with tf.variable_scope(scope, 'CifarNet', [images, num_classes], reuse=None):
        net = images
        net = slim.repeat(net, 2, max_pooled_conv, scope="max_pooled_conv")
        net = slim.flatten(net)
        net = slim.fully_connected(net, 384, normalizer_fn=slim.batch_norm, scope='fc3')
        net = slim.dropout(net, dropout_keep_prob, is_training=is_training, scope='dropout3')
        net = slim.fully_connected(net, 192, normalizer_fn=slim.batch_norm, scope='fc4')
        net = slim.fully_connected(net, num_classes,
                                   normalizer_fn=slim.batch_norm,
                                   biases_initializer=tf.zeros_initializer,
                                   weights_initializer=tf.truncated_normal_initializer(stddev=1/192.0),
                                   weights_regularizer=None,
                                   activation_fn=None,
                                   scope='logits')
    return net

logits = model(images, n_classes, is_training)
predictions = tf.nn.softmax(logits)

Question 3

What does your final architecture look like? (Type of model, layers, sizes, connectivity, etc.) For reference on how to build a deep neural network using TensorFlow, see Deep Neural Network in TensorFlow from the classroom.

Answer:

I based my architecture on the TensorFlow Slim definition of the CIFAR-10 model (https://github.com/tensorflow/models/blob/master/slim/nets/cifarnet.py), which has 2 building blocks consisting of a convulational and max pool layer; followed by a 3-layered fully connected section with dropout between the first 2 fully connected layers. As you can see from the above code cell, the layers have the following settings:

• 2-dimensional convolutional
  • kernel: 5x5
  • stride: 1 (default)
  • output size: 64
• 2D max pool:
  • kernel: 2x2
  • stride: 2 (default)
• fully connected -- output sizes of 384, 192, and num_classes (i.e., 43 for each type of traffic sign) in that order

However, the original normalization layers have been removed in favor of batch normalization set as a parameter in both the convolutional and fully connected layers. Also, the default dropout is set to 0.71875.

Run the cell below to generate the architectural diagram (NOTE: only viewable in Google Chrome browser) and be sure to click to expand the neural network boxes:

# http://stackoverflow.com/questions/38189119/simple-way-to-visualize-a-tensorflow-graph-in-jupyter
from IPython.display import clear_output, Image, display, HTML

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add() 
        n.MergeFrom(n0)
        if n.op == 'Const':
            tensor = n.attr['value'].tensor
            size = len(tensor.tensor_content)
            if size > max_const_size:
                tensor.tensor_content = "<stripped %d bytes>"%size
    return strip_def

def show_graph(graph_def, max_const_size=32):
    """Visualize TensorFlow graph."""
    if hasattr(graph_def, 'as_graph_def'):
        graph_def = graph_def.as_graph_def()
    strip_def = strip_consts(graph_def, max_const_size=max_const_size)
    code = """
        <script>
          function load() {{
            document.getElementById("{id}").pbtxt = {data};
          }}
        </script>
        <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>
        <div style="height:600px">
          <tf-graph-basic id="{id}"></tf-graph-basic>
        </div>
    """.format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))

    iframe = """
        <iframe seamless style="width:800px;height:620px;border:0" srcdoc="{}"></iframe>
    """.format(code.replace('"', '&quot;'))
    display(HTML(iframe))

show_graph(tf.get_default_graph().as_graph_def())

### Train your model here.
### Feel free to use as many code cells as needed.
import math
from tqdm import tqdm
from sklearn.utils import shuffle

def get_batch(X, y, batch_size, batch_number=0):
    start, end = batch_number*batch_size, (batch_number+1)*batch_size
    length = len(X)
    if end > length:
        end = length
    return X[start:end], y[start:end]

one_hot_encoded_labels = slim.one_hot_encoding(labels, n_classes)

def calculate_loss(logits, one_hot_encoded_labels):
    cross_entropy = slim.losses.softmax_cross_entropy(logits, one_hot_encoded_labels)
    return cross_entropy
loss = calculate_loss(logits, one_hot_encoded_labels)

def optimize(optimizer, loss):
    return optimizer.minimize(loss)
#learning_rate = 1e-4
optimizer = tf.train.AdamOptimizer()
optimize = optimize(optimizer, loss)

def calculate_accuracy(predictions, one_hot_encoded_labels):
    is_correct_prediction = tf.equal(tf.argmax(predictions, 1), tf.argmax(one_hot_encoded_labels, 1))
    accuracy = tf.reduce_mean(tf.cast(is_correct_prediction, tf.float32))
    return accuracy
accuracy = calculate_accuracy(predictions, one_hot_encoded_labels)

tf.logging.set_verbosity(tf.logging.INFO)
print("Training...")
epochs = 10
batch_size = 128
batches = []
training_loss = []
training_accuracy = []
validation_loss = []
validation_accuracy = []
saver = tf.train.Saver()
model_path = 'model.ckpt'
init = tf.initialize_all_variables()
with tf.Session() as session:  
    session.run(init)
    for epoch in range(epochs):
        training_batch_count = int(math.ceil(len(X_training) / batch_size))
        epoch_number = epoch + 1
        training_batches_progress_bar = tqdm(range(training_batch_count), 
                                             desc='Training Epoch {:>2}/{}'.format(epoch_number, epochs), 
                                             unit='batches')
        X_training, y_training = shuffle(X_training, y_training)
        for batch_number in training_batches_progress_bar:
            batch_images, batch_labels = get_batch(X_training, y_training, batch_size, batch_number)
            batch_loss, _, batch_accuracy = session.run([loss, optimize, accuracy], feed_dict={images: batch_images, 
                                                                                               labels: batch_labels, 
                                                                                               is_training: True})
            training_loss.append(batch_loss)
            training_accuracy.append(batch_accuracy)
        
        validation_batch_count = int(math.ceil(len(X_validation) / batch_size))
        validation_batches_progress_bar = tqdm(range(validation_batch_count), 
                                               desc='Validation Epoch {:>2}/{}'.format(epoch_number, epochs), 
                                               unit='batches')
        batch_validation_losses = []
        batch_validation_accuracies = []
        for batch_number in validation_batches_progress_bar:
            batch_images, batch_labels = get_batch(X_validation, y_validation, batch_size, batch_number)
            batch_validation_loss, _, batch_validation_accuracy = session.run([loss, optimize, accuracy], 
                                                                              feed_dict={images: batch_images,
                                                                                         labels: batch_labels,
                                                                                         is_training: False})
            batch_validation_losses.append(batch_validation_loss)
            batch_validation_accuracies.append(batch_validation_accuracy)
        epoch_validation_loss = np.mean(batch_validation_losses)
        epoch_validation_accuracy = np.mean(batch_validation_accuracies)
        validation_loss.append(epoch_validation_loss)
        validation_accuracy.append(epoch_validation_accuracy)
        batches.append(epoch_number * training_batch_count) # ensure that epoch validation measurements are on same x-scale as training
    print("Validation Loss: {}, Accuracy: {}".format(validation_loss[-1], validation_accuracy[-1]))
    print("Saving model...")
    saver.save(session, model_path)
print("Testing...")
test_loss = []
test_accuracy = []
with tf.Session() as session:  
    session.run(init)
    print("Loading saved model...")
    saver.restore(session, model_path)
    batch_count = int(math.ceil(len(X_test) / batch_size))
    batches_progress_bar = tqdm(range(batch_count),
                                desc='Epoch {:>2}/{}'.format(1, 1),
                                unit='batches')
    for batch_number in batches_progress_bar:
        batch_images, batch_labels = get_batch(X_test_normalized, y_test, batch_size, batch_number)
        batch_loss, _, batch_accuracy = session.run([loss, optimize, accuracy], feed_dict={images: batch_images, 
                                                                                           labels: batch_labels, 
                                                                                           is_training: False})
        test_loss.append(batch_loss)
        test_accuracy.append(batch_accuracy)
    print("Test Loss: {}, Accuracy: {}".format(np.mean(test_loss), np.mean(test_accuracy)))

Training...

Training Epoch  1/10: 100%|██████████| 246/246 [00:05<00:00, 45.52batches/s]
Validation Epoch  1/10: 100%|██████████| 62/62 [00:01<00:00, 52.53batches/s]
Training Epoch  2/10: 100%|██████████| 246/246 [00:04<00:00, 51.24batches/s]
Validation Epoch  2/10: 100%|██████████| 62/62 [00:01<00:00, 52.94batches/s]
Training Epoch  3/10: 100%|██████████| 246/246 [00:04<00:00, 51.04batches/s]
Validation Epoch  3/10: 100%|██████████| 62/62 [00:01<00:00, 53.23batches/s]
Training Epoch  4/10: 100%|██████████| 246/246 [00:04<00:00, 50.90batches/s]
Validation Epoch  4/10: 100%|██████████| 62/62 [00:01<00:00, 53.11batches/s]
Training Epoch  5/10: 100%|██████████| 246/246 [00:04<00:00, 50.87batches/s]
Validation Epoch  5/10: 100%|██████████| 62/62 [00:01<00:00, 53.31batches/s]
Training Epoch  6/10: 100%|██████████| 246/246 [00:04<00:00, 50.78batches/s]
Validation Epoch  6/10: 100%|██████████| 62/62 [00:01<00:00, 53.08batches/s]
Training Epoch  7/10: 100%|██████████| 246/246 [00:04<00:00, 51.11batches/s]
Validation Epoch  7/10: 100%|██████████| 62/62 [00:01<00:00, 52.97batches/s]
Training Epoch  8/10: 100%|██████████| 246/246 [00:04<00:00, 51.08batches/s]
Validation Epoch  8/10: 100%|██████████| 62/62 [00:01<00:00, 53.01batches/s]
Training Epoch  9/10: 100%|██████████| 246/246 [00:04<00:00, 50.93batches/s]
Validation Epoch  9/10: 100%|██████████| 62/62 [00:01<00:00, 53.04batches/s]
Training Epoch 10/10: 100%|██████████| 246/246 [00:04<00:00, 50.82batches/s]
Validation Epoch 10/10: 100%|██████████| 62/62 [00:01<00:00, 52.96batches/s]

Validation Loss: 0.21909159421920776, Accuracy: 0.999243974685669
Saving model...
Testing...
Loading saved model...

Epoch  1/1: 100%|██████████| 99/99 [00:01<00:00, 49.59batches/s]

Test Loss: 0.29548555612564087, Accuracy: 0.9833894968032837

%matplotlib notebook
print("Plotting metrics...")
loss_plot = plt.subplot(211)
loss_plot.set_title('Loss')
loss_plot.plot(training_loss, 'r', label='Training Loss')
loss_plot.plot(batches, validation_loss, 'b', label='Validation Loss')
loss_plot.legend(loc=1)
accuracy_plot = plt.subplot(212)
accuracy_plot.set_title('Accuracy')
accuracy_plot.plot(training_accuracy, 'r', label='Training Accuracy')
accuracy_plot.plot(batches, validation_accuracy, 'b', label='Validation Accuracy')
accuracy_plot.set_ylim([0, 1.0])
accuracy_plot.legend(loc=4)
plt.tight_layout()
plt.show()

Plotting metrics...

Question 4

How did you train your model? (Type of optimizer, batch size, epochs, hyperparameters, etc.)

Answer:

Found the below optimal values based on incrementally trying different values and seeing their effects on validation & test losses and accuracies:

• Optimizer: Adam (default learning rate and other parameters) -- recommended as one of the first optimizers to try nowadays (http://cs231n.github.io/neural-networks-3/#summary)
• Batch Size: 128 -- Bigger batches can probably fit in the 12GB of memory in my Titan X Pascal GPU, but would require setting the learning rate to be much smaller and risk numerical stability. Plus, I haven't seen huge batch sizes in the deep learning literature.
• Epochs: 10 -- Although training & validation accuracy seems to plateau before 10 epochs, I noticed the loss metrics and the test accuracy reached a local optimum later.
• Dropout: 0.71875

Things to try later:

• Data Augmentation
• Ensembling different instances of the same model each independently trained on one of the stratified k-fold training/validation splits
• Pseudo-Labeling
• More modern architecture like Inception and/or ResNet

Question 5

What approach did you take in coming up with a solution to this problem?

Answer:

Using the TensorFlow lab notebook as a template to start from, as well as using TensorFlow's Slim API to more quickly define an architecture, I iteratively refined the model by looking at the effects on validation and test losses & accuracies when trying the following:

• Started with a simple 1-layer (fully-connected) model.
• Tried Cifar-10 model -- without pre-trained weights -- defined in https://github.com/tensorflow/models/blob/master/slim/nets/cifarnet.py since it also accepts 32x32 images
• Changed to batch norm layers
• Tried different epochs, dropout & learning rates to see which would result in the optimal validation and test losses & accuracies.

In consideration of time constraints, trying bigger architectures like Inception and ResNet would require longer training times, so let's stick with this current architecture for now.

---

Step 3: Test a Model on New Images

Take several pictures of traffic signs that you find on the web or around you (at least five), and run them through your classifier on your computer to produce example results. The classifier might not recognize some local signs but it could prove interesting nonetheless.

You may find signnames.csv useful as it contains mappings from the class id (integer) to the actual sign name.

Implementation

Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow.

### Load the images and plot them here.
### Feel free to use as many code cells as needed.
from scipy.misc import imread
%matplotlib inline
f = plt.figure(figsize=(12,5))
filenames = ['do_not_enter','pedestrian_crossing', 'stop', 'yield', 'road_work']
filenames_count = len(filenames)
rows = 1
new_test_images = []
for i, filename in enumerate(filenames):
    sp = f.add_subplot(1, filenames_count//rows, i+1)
    img = imread(filename + '.png', mode='RGB')
    #print(type(img), img.shape)
    new_test_images.append(img)
    plt.imshow(img)

Question 6

Choose five candidate images of traffic signs and provide them in the report. Are there any particular qualities of the image(s) that might make classification difficult? It would be helpful to plot the images in the notebook.

Answer:

Some of the U.S. signs might not exactly match any of the German signs that the model was trained on. The inclusion of other objects in the images may confuse the model. The lighting, weather, and environmental conditions of the photos I take may also not be similar enough to the training images.

### Run the predictions here.
### Feel free to use as many code cells as needed.

# normalize new test images
new_test_images_numpy = np.array(new_test_images)
new_test_images_reshaped = np.reshape(new_test_images_numpy.astype('float32'), (len(new_test_images_numpy), -1))

new_test_images_reshaped_normalized = standard_scaler.transform(new_test_images_reshaped)
new_test_images_normalized = new_test_images_reshaped_normalized.reshape(-1, image_shape[0], image_shape[1], image_shape[2])

with tf.Session() as session:  
    session.run(init)
    print("Loading saved model...")
    saver.restore(session, model_path)
    print("Predicting...")
    new_predictions = session.run(predictions, feed_dict={images: new_test_images_normalized,
                                                            #labels: None,
                                                            is_training: False})

Loading saved model...
Predicting...

predicted_sign_ids = np.argmax(new_predictions, axis=1)
for i, filename in enumerate(filenames):
    sign_id = predicted_sign_ids[i]
    print("Image: {}, Prediction: {} (sign ID {}), ".format(filename, signnames_dataframe.SignName[sign_id], sign_id))
f = plt.figure(figsize=(12,5))
plt.title("Example Predicted Sign Types")
prediction_count = 5
rows = 1
for i, predicted_sign_id in enumerate(predicted_sign_ids):
    training_image_index = next(i for i, label in enumerate(y_train) if label == predicted_sign_id)
    sp = f.add_subplot(1, prediction_count//rows, i+1)
    plt.imshow(X_train[training_image_index])

Image: do_not_enter, Prediction: No passing for vechiles over 3.5 metric tons (sign ID 10), 
Image: pedestrian_crossing, Prediction: Priority road (sign ID 12), 
Image: stop, Prediction: Yield (sign ID 13), 
Image: yield, Prediction: Keep right (sign ID 38), 
Image: road_work, Prediction: Speed limit (70km/h) (sign ID 4), 

f = plt.figure(figsize=(12,5))
plt.title("Example Expected Sign Types")
for i, expected_sign_id in enumerate([17, 27, 14, 13, 25]):
    training_image_index = next(i for i, label in enumerate(y_train) if label == expected_sign_id)
    sp = f.add_subplot(1, prediction_count//rows, i+1)
    plt.imshow(X_train[training_image_index])

Question 7

Is your model able to perform equally well on captured pictures or a live camera stream when compared to testing on the dataset?

Answer:

The top-1 accuracy doesn't look good for the 5 new test images. None of the new images matched their German counterparts. However, note the following:

• The model may have confused the red circle in the "do not enter" sign with other red circles seen in the German signs.
• The yellow diamond pedestrian sign resembles the German yellow diamond sign.

The previously calculated loss & accuracy on the test dataset indicates that the model was trained well, and performed well under testing, for German signs, but does not generalize well to non-German signs. Perhaps a bigger variety of training images (including data augmentaiton of the given German training images with incremental random changes in brightness, rotation, scaling, horizontal & vertical shifting), and a bigger neural network architecture, would help classify the new test images more accurately.

### Visualize the softmax probabilities here.
### Feel free to use as many code cells as needed.
top_predictions = tf.nn.top_k(predictions, k=5)

with tf.Session() as session:  
    session.run(init)
    print("Loading saved model...")
    saver.restore(session, model_path)
    print("Predicting...")
    top_new_predictions = session.run(top_predictions, feed_dict={images: new_test_images_normalized,
                                                            #labels: None,
                                                            is_training: False})
top_prediction_values, top_prediction_indices = top_new_predictions
f = plt.figure(figsize=(15,20))
#plt.title("Top 5 Predictions")
y_positions = np.arange(5)
sp = None
for i in range(prediction_count):
    sp = f.add_subplot(5, 1, i+1, sharex=sp)
    top_signnames = [signnames_dataframe.SignName[sign_id] for sign_id in top_prediction_indices[i]]
    top_values = top_prediction_values[i]
    plt.barh(y_positions, top_values[::-1]) # reverse the order so that highest is on top
    plt.yticks(y_positions, top_signnames[::-1])
    plt.title(filenames[i])

Loading saved model...
Predicting...

Question 8

Use the model's softmax probabilities to visualize the certainty of its predictions, tf.nn.top_k could prove helpful here. Which predictions is the model certain of? Uncertain? If the model was incorrect in its initial prediction, does the correct prediction appear in the top k? (k should be 5 at most)

Answer:

• Because none of the prediction values are close to 100%, the model seems mostly uncertain of the classifications of the new test images that we've just given it.
• For incorrect initial predictions, the only correct prediction to appear in the top 5 was for "do not enter/no entry".

Question 9

If necessary, provide documentation for how an interface was built for your model to load and classify newly-acquired images.

Answer:

(Not applicable)

Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.